e06 High bone density: too much of a good thing?
Bibliographic record
Abstract
Background: We would like to present the case of a 49 year old female referred to the osteoporosis clinic following a bone scan that demonstrated intense, diffuse symmetrical uptake, in keeping with a metabolic super scan. The bone scan was prompted following an incidental finding of increased bone density on chest radiograph. Methods: A CT chest, abdo, pelvis also demonstrated widespread increased bone density, with no abnormalities seen in the spleen, liver of kidneys. No evidence of lymph nodes was noted. She had a background of psoriatic arthritis not on DMARD therapy, congenital cataracts, vitamin B12 deficiency (treated) and Downs’s syndrome. She denied any history of urticarial rash or old factory dysfunction. There is no history of bowel disturbances, recurrent infections of fractures. She denied increased fluoride intake, or large amounts of tea consumption. There is a family history in her mother of osteoporosis, but nil else of note. The endocrinologists have reviewed her and excluded any endocrine cause for her high bone density. DEXA scan conducted revealed elevated Z and T scores suggestive of high bone mass, spine predominant. Lumber spine T score 5.3 and Z score 6. Neck of femur both T and Z scores 1.7. Differentials include myelosclerosis, fluorosis, mastocytosis, hyperparathyroidism, osteomalacia, renal osteodystrophy or widespread Paget’s disease. Blood test showed normal FBC, PTH, TSH and anti-TTG was negative, Vitamin D 82. Alkaline phosphatase remained persistently high at 286. Protein electrophoresis was normal. Hepatitis C screen negative. Serum tryptase within normal limits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".